Surgery and Cancer, Biomolecular Medicine, Imperial College London
Doi
Abstract
The interpretation of metabolic information is crucial to understanding the functioning of a biological
system. Latent information about the metabolic state of a sample can be acquired using
analytical chemistry methods, which generate spectroscopic profiles. Thus, nuclear magnetic resonance
spectroscopy and mass spectrometry techniques can be employed to generate vast amounts
of highly complex data on the metabolic content of biofluids and tissue, and this thesis discusses
ways to process, analyse and interpret these data successfully.
The evaluation of J -resolved spectroscopy in magnetic resonance profiling and the statistical
techniques required to extract maximum information from the projections of these spectra are
studied. In particular, data processing is evaluated, and correlation and regression methods are
investigated with respect to enhanced model interpretation and biomarker identification. Additionally,
it is shown that non-linearities in metabonomic data can be effectively modelled with
kernel-based orthogonal partial least squares, for which an automated optimisation of the kernel
parameter with nested cross-validation is implemented. The interpretation of orthogonal variation
and predictive ability enabled by this approach are demonstrated in regression and classification
models for applications in toxicology and parasitology. Finally, the vast amount of data generated
with mass spectrometry imaging is investigated in terms of data processing, and the benefits of
applying multivariate techniques to these data are illustrated, especially in terms of interpretation
and visualisation using colour-coding of images. The advantages of methods such as principal
component analysis, self-organising maps and manifold learning over univariate analysis are highlighted.
This body of work therefore demonstrates new means of increasing the amount of biochemical
information that can be obtained from a given set of samples in biological applications using
spectral profiling. Various analytical and statistical methods are investigated and illustrated with
applications drawn from diverse biomedical areas